ANCOVA in Vocabulary
(Vocabulary)
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
Vocabulary (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in Vocabulary (measured using pre- and post-tests).
Setting Initial Variables
dv = "score.vocab"
dv.pos = "score.vocab.pos"
dv.pre = "score.vocab.pre"
fatores2 <- c("genero","zona.participante","zona.escola","score.vocab.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["genero"]] = c("#FF007F","#4D4DFF")
color[["zona.escola"]] = c("#AA00FF","#00CCCC")
color[["zona.participante"]] = c("#AA00FF","#00CCCC")
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["genero"]] = c("F","M")
level[["zona.escola"]] = c("Rural","Urbana")
level[["zona.participante"]] = c("Rural","Urbana")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:genero"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:zona.escola"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:zona.participante"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
for (coln in c(
"palavras.lidas","score.compreensao","tri.compreensao",
"score.vocab","tri.vocab",
"score.vocab.ensinado","tri.vocab.ensinado","score.vocab.nao.ensinado","tri.vocab.nao.ensinado",
"score.CLPP","tri.CLPP","score.CR","tri.CR",
"score.CI","tri.CI","score.TV","tri.TV","score.TF","tri.TF","score.TO","tri.TO")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "vocabulario.st")
dat <- gdat
dat$grupo <- factor(dat[["grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.vocab.pre |
44 |
13.364 |
13.0 |
5 |
23 |
4.725 |
0.712 |
1.437 |
6.25 |
YES |
0.329 |
-0.650 |
| Experimental |
|
|
|
|
score.vocab.pre |
39 |
13.205 |
13.0 |
6 |
22 |
3.427 |
0.549 |
1.111 |
4.00 |
YES |
0.445 |
-0.237 |
|
|
|
|
|
score.vocab.pre |
83 |
13.289 |
13.0 |
5 |
23 |
4.142 |
0.455 |
0.905 |
6.00 |
YES |
0.395 |
-0.271 |
| Controle |
|
|
|
|
score.vocab.pos |
44 |
15.295 |
15.0 |
4 |
26 |
4.878 |
0.735 |
1.483 |
6.25 |
YES |
0.295 |
-0.376 |
| Experimental |
|
|
|
|
score.vocab.pos |
39 |
14.333 |
14.0 |
6 |
25 |
4.544 |
0.728 |
1.473 |
4.50 |
NO |
0.554 |
0.122 |
|
|
|
|
|
score.vocab.pos |
83 |
14.843 |
14.0 |
4 |
26 |
4.720 |
0.518 |
1.031 |
5.00 |
YES |
0.426 |
-0.128 |
| Controle |
F |
|
|
|
score.vocab.pre |
23 |
14.348 |
14.0 |
7 |
23 |
4.519 |
0.942 |
1.954 |
7.00 |
YES |
0.342 |
-0.917 |
| Controle |
M |
|
|
|
score.vocab.pre |
21 |
12.286 |
12.0 |
5 |
23 |
4.818 |
1.051 |
2.193 |
5.00 |
YES |
0.418 |
-0.608 |
| Experimental |
F |
|
|
|
score.vocab.pre |
17 |
12.353 |
12.0 |
6 |
22 |
3.517 |
0.853 |
1.808 |
3.00 |
NO |
0.941 |
1.239 |
| Experimental |
M |
|
|
|
score.vocab.pre |
22 |
13.864 |
14.0 |
9 |
20 |
3.285 |
0.700 |
1.456 |
5.00 |
YES |
0.084 |
-1.211 |
| Controle |
F |
|
|
|
score.vocab.pos |
23 |
16.696 |
16.0 |
8 |
26 |
4.922 |
1.026 |
2.128 |
8.00 |
YES |
0.285 |
-1.082 |
| Controle |
M |
|
|
|
score.vocab.pos |
21 |
13.762 |
13.0 |
4 |
24 |
4.449 |
0.971 |
2.025 |
4.00 |
YES |
0.173 |
0.041 |
| Experimental |
F |
|
|
|
score.vocab.pos |
17 |
14.118 |
14.0 |
6 |
24 |
4.241 |
1.029 |
2.180 |
6.00 |
YES |
0.303 |
-0.153 |
| Experimental |
M |
|
|
|
score.vocab.pos |
22 |
14.500 |
14.0 |
7 |
25 |
4.857 |
1.036 |
2.154 |
3.75 |
NO |
0.625 |
-0.112 |
| Controle |
|
Rural |
|
|
score.vocab.pre |
11 |
10.727 |
10.0 |
5 |
16 |
3.467 |
1.045 |
2.329 |
4.00 |
YES |
-0.119 |
-1.265 |
| Controle |
|
Urbana |
|
|
score.vocab.pre |
22 |
15.545 |
15.5 |
7 |
23 |
4.667 |
0.995 |
2.069 |
6.75 |
YES |
-0.025 |
-1.075 |
| Controle |
|
|
|
|
score.vocab.pre |
11 |
11.636 |
11.0 |
6 |
21 |
4.105 |
1.238 |
2.758 |
2.00 |
NO |
0.655 |
0.151 |
| Experimental |
|
Rural |
|
|
score.vocab.pre |
15 |
13.733 |
13.0 |
6 |
22 |
4.026 |
1.040 |
2.230 |
5.50 |
YES |
0.212 |
-0.551 |
| Experimental |
|
Urbana |
|
|
score.vocab.pre |
14 |
12.429 |
11.5 |
9 |
18 |
2.954 |
0.789 |
1.706 |
4.75 |
YES |
0.491 |
-1.233 |
| Experimental |
|
|
|
|
score.vocab.pre |
10 |
13.500 |
14.0 |
9 |
20 |
3.206 |
1.014 |
2.293 |
3.50 |
YES |
0.428 |
-0.696 |
| Controle |
|
Rural |
|
|
score.vocab.pos |
11 |
15.364 |
13.0 |
10 |
26 |
4.864 |
1.466 |
3.267 |
5.00 |
NO |
0.956 |
-0.396 |
| Controle |
|
Urbana |
|
|
score.vocab.pos |
22 |
16.273 |
16.0 |
4 |
24 |
5.539 |
1.181 |
2.456 |
8.00 |
YES |
-0.252 |
-0.861 |
| Controle |
|
|
|
|
score.vocab.pos |
11 |
13.273 |
15.0 |
8 |
16 |
2.760 |
0.832 |
1.854 |
3.00 |
NO |
-0.724 |
-1.030 |
| Experimental |
|
Rural |
|
|
score.vocab.pos |
15 |
14.933 |
15.0 |
6 |
24 |
4.399 |
1.136 |
2.436 |
4.50 |
YES |
0.225 |
-0.118 |
| Experimental |
|
Urbana |
|
|
score.vocab.pos |
14 |
14.929 |
14.5 |
7 |
25 |
5.385 |
1.439 |
3.109 |
6.00 |
NO |
0.511 |
-0.669 |
| Experimental |
|
|
|
|
score.vocab.pos |
10 |
12.600 |
13.5 |
8 |
18 |
3.273 |
1.035 |
2.341 |
4.50 |
YES |
0.029 |
-1.444 |
| Controle |
|
|
Rural |
|
score.vocab.pre |
13 |
11.615 |
10.0 |
5 |
21 |
4.874 |
1.352 |
2.945 |
7.00 |
YES |
0.311 |
-1.180 |
| Controle |
|
|
Urbana |
|
score.vocab.pre |
31 |
14.097 |
13.0 |
6 |
23 |
4.541 |
0.816 |
1.666 |
6.50 |
YES |
0.431 |
-0.690 |
| Experimental |
|
|
Rural |
|
score.vocab.pre |
12 |
13.000 |
13.5 |
9 |
16 |
2.216 |
0.640 |
1.408 |
3.25 |
YES |
-0.184 |
-1.319 |
| Experimental |
|
|
Urbana |
|
score.vocab.pre |
27 |
13.296 |
12.0 |
6 |
22 |
3.881 |
0.747 |
1.535 |
6.00 |
YES |
0.411 |
-0.699 |
| Controle |
|
|
Rural |
|
score.vocab.pos |
13 |
15.231 |
15.0 |
8 |
26 |
5.052 |
1.401 |
3.053 |
5.00 |
NO |
0.666 |
-0.520 |
| Controle |
|
|
Urbana |
|
score.vocab.pos |
31 |
15.323 |
15.0 |
4 |
24 |
4.888 |
0.878 |
1.793 |
7.00 |
YES |
0.108 |
-0.480 |
| Experimental |
|
|
Rural |
|
score.vocab.pos |
12 |
13.417 |
14.0 |
6 |
24 |
4.889 |
1.411 |
3.106 |
7.00 |
YES |
0.462 |
-0.484 |
| Experimental |
|
|
Urbana |
|
score.vocab.pos |
27 |
14.741 |
14.0 |
7 |
25 |
4.417 |
0.850 |
1.747 |
4.50 |
NO |
0.641 |
0.189 |
| Controle |
|
|
|
1st quintile |
score.vocab.pre |
21 |
9.429 |
10.0 |
5 |
12 |
2.204 |
0.481 |
1.003 |
2.00 |
NO |
-0.630 |
-0.925 |
| Controle |
|
|
|
2nd quintile |
score.vocab.pre |
19 |
15.789 |
15.0 |
13 |
19 |
2.226 |
0.511 |
1.073 |
3.50 |
YES |
0.222 |
-1.461 |
| Controle |
|
|
|
3rd quintile |
score.vocab.pre |
4 |
22.500 |
23.0 |
21 |
23 |
1.000 |
0.500 |
1.591 |
0.50 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.vocab.pre |
19 |
10.368 |
11.0 |
6 |
12 |
1.461 |
0.335 |
0.704 |
1.00 |
NO |
-1.222 |
1.668 |
| Experimental |
|
|
|
2nd quintile |
score.vocab.pre |
18 |
15.333 |
15.0 |
13 |
19 |
1.715 |
0.404 |
0.853 |
2.75 |
YES |
0.499 |
-0.881 |
| Experimental |
|
|
|
3rd quintile |
score.vocab.pre |
2 |
21.000 |
21.0 |
20 |
22 |
1.414 |
1.000 |
12.706 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
1st quintile |
score.vocab.pos |
21 |
13.190 |
13.0 |
4 |
22 |
3.683 |
0.804 |
1.676 |
3.00 |
YES |
-0.115 |
0.785 |
| Controle |
|
|
|
2nd quintile |
score.vocab.pos |
19 |
16.895 |
16.0 |
8 |
26 |
4.988 |
1.144 |
2.404 |
6.50 |
YES |
0.011 |
-1.015 |
| Controle |
|
|
|
3rd quintile |
score.vocab.pos |
4 |
18.750 |
19.5 |
12 |
24 |
6.185 |
3.092 |
9.841 |
9.75 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.vocab.pos |
19 |
13.421 |
13.0 |
6 |
25 |
5.157 |
1.183 |
2.485 |
5.50 |
NO |
0.733 |
-0.181 |
| Experimental |
|
|
|
2nd quintile |
score.vocab.pos |
18 |
15.167 |
15.0 |
9 |
25 |
3.989 |
0.940 |
1.984 |
2.00 |
NO |
0.646 |
0.283 |
| Experimental |
|
|
|
3rd quintile |
score.vocab.pos |
2 |
15.500 |
15.5 |
14 |
17 |
2.121 |
1.500 |
19.059 |
1.50 |
few data |
0.000 |
0.000 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "score.vocab.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab"]] <- c(pdat[["score.vocab.pre"]], pdat[["score.vocab.pos"]])
aov = anova_test(pdat, score.vocab.pos ~ score.vocab.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, score.vocab.pos ~ grupo, covariate = score.vocab.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "score.vocab.pos", "grupo", covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.vocab.pre","se.score.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(score.vocab.pos ~ score.vocab.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab"]] <- c(wdat[["score.vocab.pre"]], wdat[["score.vocab.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## NULL
aov = anova_test(wdat, score.vocab.pos ~ score.vocab.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 score.vocab.pre 1 80 16.154 0.000131 * 0.168
## 2 grupo 1 80 0.868 0.354000 0.011
| score.vocab.pre |
1 |
80 |
16.154 |
0.000 |
* |
0.168 |
| grupo |
1 |
80 |
0.868 |
0.354 |
|
0.011 |
pwc <- emmeans_test(wdat, score.vocab.pos ~ grupo, covariate = score.vocab.pre,
p.adjust.method = "bonferroni")
| score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
80 |
0.932 |
0.354 |
0.354 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
score.vocab |
pre |
pos |
162 |
-2.034 |
0.044 |
0.044 |
* |
| Experimental |
time |
score.vocab |
pre |
pos |
162 |
-1.118 |
0.265 |
0.265 |
ns |
ds <- get.descriptives(wdat, "score.vocab.pos", "grupo", covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.vocab.pre","se.score.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
44 |
13.364 |
0.712 |
15.295 |
0.735 |
15.261 |
0.654 |
13.96 |
16.562 |
| Experimental |
39 |
13.205 |
0.549 |
14.333 |
0.728 |
14.372 |
0.694 |
12.99 |
15.754 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "score.vocab.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] + ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "score.vocab.pos", "grupo", aov, pwc, covar = "score.vocab.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "score.vocab", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(score.vocab.pos ~ score.vocab.pre + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.975 0.108
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 81 0.0395 0.843
ANCOVA and
Pairwise for two factors grupo:genero
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["genero"]]),],
"score.vocab.pos", c("grupo","genero"))
pdat = pdat[pdat[["genero"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["genero"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["genero"]] = factor(
pdat[["genero"]],
level[["genero"]][level[["genero"]] %in% unique(pdat[["genero"]])])
pdat.long <- rbind(pdat[,c("id","grupo","genero")], pdat[,c("id","grupo","genero")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab"]] <- c(pdat[["score.vocab.pre"]], pdat[["score.vocab.pos"]])
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(pdat, score.vocab.pos ~ score.vocab.pre + grupo*genero)
laov[["grupo:genero"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(pdat, grupo), score.vocab.pos ~ genero,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, genero), score.vocab.pos ~ grupo,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","genero")),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(pdat, "score.vocab.pos", c("grupo","genero"), covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.vocab.pre","se.score.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["genero"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.vocab.pos ~ score.vocab.pre + grupo*genero, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","genero")], wdat[,c("id","grupo","genero")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab"]] <- c(wdat[["score.vocab.pre"]], wdat[["score.vocab.pos"]])
ldat[["grupo:genero"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(wdat, score.vocab.pos ~ score.vocab.pre + grupo*genero)
laov[["grupo:genero"]] <- merge(get_anova_table(aov), laov[["grupo:genero"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.vocab.pre |
1 |
78 |
13.518 |
0.000 |
* |
0.148 |
| grupo |
1 |
78 |
0.676 |
0.414 |
|
0.009 |
| genero |
1 |
78 |
1.596 |
0.210 |
|
0.020 |
| grupo:genero |
1 |
78 |
0.811 |
0.371 |
|
0.010 |
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(wdat, grupo), score.vocab.pos ~ genero,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, genero), score.vocab.pos ~ grupo,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
|
F |
score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
78 |
1.219 |
0.226 |
0.226 |
ns |
|
M |
score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
78 |
-0.039 |
0.969 |
0.969 |
ns |
| Controle |
|
score.vocab.pre*genero |
score.vocab.pos |
F |
M |
78 |
1.534 |
0.129 |
0.129 |
ns |
| Experimental |
|
score.vocab.pre*genero |
score.vocab.pos |
F |
M |
78 |
0.195 |
0.846 |
0.846 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","genero")),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:genero"]],
by=c("grupo","genero","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
score.vocab |
pre |
pos |
158 |
-1.812 |
0.072 |
0.072 |
ns |
| Controle |
M |
time |
score.vocab |
pre |
pos |
158 |
-1.089 |
0.278 |
0.278 |
ns |
| Experimental |
F |
time |
score.vocab |
pre |
pos |
158 |
-1.171 |
0.243 |
0.243 |
ns |
| Experimental |
M |
time |
score.vocab |
pre |
pos |
158 |
-0.480 |
0.632 |
0.632 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(wdat, "score.vocab.pos", c("grupo","genero"), covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.vocab.pre","se.score.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- merge(ds, lemms[["grupo:genero"]],
by=c("grupo","genero"), suffixes = c("","'"))
}
| Controle |
F |
23 |
14.348 |
0.942 |
16.696 |
1.026 |
16.235 |
0.910 |
14.423 |
18.048 |
| Controle |
M |
21 |
12.286 |
1.051 |
13.762 |
0.971 |
14.198 |
0.951 |
12.304 |
16.092 |
| Experimental |
F |
17 |
12.353 |
0.853 |
14.118 |
1.029 |
14.525 |
1.055 |
12.425 |
16.625 |
| Experimental |
M |
22 |
13.864 |
0.700 |
14.500 |
1.036 |
14.250 |
0.925 |
12.410 |
16.091 |
Plots for ancova
if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "genero", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "genero", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.vocab.pos", c("grupo","genero"), aov, pwcs, covar = "score.vocab.pre",
theme = "classic", color = color[["grupo:genero"]],
subtitle = which(aov$Effect == "grupo:genero"))
}
if (length(unique(pdat[["genero"]])) >= 2) {
plots[["grupo:genero"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.vocab", c("grupo","genero"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["genero"]])) >= 2)
plots[["grupo:genero"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
facet.by = c("grupo","genero"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "grupo", facet.by = "genero", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "genero", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = genero)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["genero"]])) >= 2)
res <- augment(lm(score.vocab.pos ~ score.vocab.pre + grupo*genero, data = wdat))
if (length(unique(pdat[["genero"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.982 0.281
if (length(unique(pdat[["genero"]])) >= 2)
levene_test(res, .resid ~ grupo*genero)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 79 0.605 0.614
ANCOVA
and Pairwise for two factors
grupo:zona.participante
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.participante"]]),],
"score.vocab.pos", c("grupo","zona.participante"))
pdat = pdat[pdat[["zona.participante"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.participante"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.participante"]] = factor(
pdat[["zona.participante"]],
level[["zona.participante"]][level[["zona.participante"]] %in% unique(pdat[["zona.participante"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.participante")], pdat[,c("id","grupo","zona.participante")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab"]] <- c(pdat[["score.vocab.pre"]], pdat[["score.vocab.pos"]])
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(pdat, score.vocab.pos ~ score.vocab.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(pdat, grupo), score.vocab.pos ~ zona.participante,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.participante), score.vocab.pos ~ grupo,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.participante")),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(pdat, "score.vocab.pos", c("grupo","zona.participante"), covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.vocab.pre","se.score.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.participante"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.vocab.pos ~ score.vocab.pre + grupo*zona.participante, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.participante")], wdat[,c("id","grupo","zona.participante")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab"]] <- c(wdat[["score.vocab.pre"]], wdat[["score.vocab.pos"]])
ldat[["grupo:zona.participante"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(wdat, score.vocab.pos ~ score.vocab.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- merge(get_anova_table(aov), laov[["grupo:zona.participante"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.vocab.pre |
1 |
57 |
13.732 |
0.000 |
* |
0.194 |
| grupo |
1 |
57 |
0.330 |
0.568 |
|
0.006 |
| zona.participante |
1 |
57 |
0.136 |
0.714 |
|
0.002 |
| grupo:zona.participante |
1 |
57 |
0.967 |
0.329 |
|
0.017 |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(wdat, grupo), score.vocab.pos ~ zona.participante,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.participante), score.vocab.pos ~ grupo,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
|
Rural |
score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
57 |
1.125 |
0.265 |
0.265 |
ns |
|
Urbana |
score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
57 |
-0.258 |
0.797 |
0.797 |
ns |
| Controle |
|
score.vocab.pre*zona.participante |
score.vocab.pos |
Rural |
Urbana |
57 |
0.979 |
0.332 |
0.332 |
ns |
| Experimental |
|
score.vocab.pre*zona.participante |
score.vocab.pos |
Rural |
Urbana |
57 |
-0.424 |
0.673 |
0.673 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.participante")),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.participante"]],
by=c("grupo","zona.participante","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.vocab |
pre |
pos |
116 |
-2.368 |
0.020 |
0.020 |
* |
| Controle |
Urbana |
time |
score.vocab |
pre |
pos |
116 |
-0.525 |
0.600 |
0.600 |
ns |
| Experimental |
Rural |
time |
score.vocab |
pre |
pos |
116 |
-0.716 |
0.476 |
0.476 |
ns |
| Experimental |
Urbana |
time |
score.vocab |
pre |
pos |
116 |
-1.440 |
0.153 |
0.153 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(wdat, "score.vocab.pos", c("grupo","zona.participante"), covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.vocab.pre","se.score.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- merge(ds, lemms[["grupo:zona.participante"]],
by=c("grupo","zona.participante"), suffixes = c("","'"))
}
| Controle |
Rural |
11 |
10.727 |
1.045 |
15.364 |
1.466 |
16.968 |
1.467 |
14.030 |
19.905 |
| Controle |
Urbana |
22 |
15.545 |
0.995 |
16.273 |
1.181 |
15.137 |
1.037 |
13.060 |
17.215 |
| Experimental |
Rural |
15 |
13.733 |
1.040 |
14.933 |
1.136 |
14.828 |
1.201 |
12.424 |
17.233 |
| Experimental |
Urbana |
14 |
12.429 |
0.789 |
14.929 |
1.439 |
15.565 |
1.254 |
13.054 |
18.077 |
Plots for ancova
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.participante", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.participante", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.vocab.pos", c("grupo","zona.participante"), aov, pwcs, covar = "score.vocab.pre",
theme = "classic", color = color[["grupo:zona.participante"]],
subtitle = which(aov$Effect == "grupo:zona.participante"))
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots[["grupo:zona.participante"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.vocab", c("grupo","zona.participante"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.participante"]])) >= 2)
plots[["grupo:zona.participante"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
facet.by = c("grupo","zona.participante"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "grupo", facet.by = "zona.participante", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "zona.participante", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.participante)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.participante"]])) >= 2)
res <- augment(lm(score.vocab.pos ~ score.vocab.pre + grupo*zona.participante, data = wdat))
if (length(unique(pdat[["zona.participante"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.979 0.367
if (length(unique(pdat[["zona.participante"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.participante)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 58 0.0846 0.968
ANCOVA and
Pairwise for two factors grupo:zona.escola
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.escola"]]),],
"score.vocab.pos", c("grupo","zona.escola"))
pdat = pdat[pdat[["zona.escola"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.escola"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.escola"]] = factor(
pdat[["zona.escola"]],
level[["zona.escola"]][level[["zona.escola"]] %in% unique(pdat[["zona.escola"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.escola")], pdat[,c("id","grupo","zona.escola")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab"]] <- c(pdat[["score.vocab.pre"]], pdat[["score.vocab.pos"]])
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(pdat, score.vocab.pos ~ score.vocab.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(pdat, grupo), score.vocab.pos ~ zona.escola,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.escola), score.vocab.pos ~ grupo,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.escola")),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(pdat, "score.vocab.pos", c("grupo","zona.escola"), covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.vocab.pre","se.score.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.escola"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.vocab.pos ~ score.vocab.pre + grupo*zona.escola, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.escola")], wdat[,c("id","grupo","zona.escola")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab"]] <- c(wdat[["score.vocab.pre"]], wdat[["score.vocab.pos"]])
ldat[["grupo:zona.escola"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(wdat, score.vocab.pos ~ score.vocab.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- merge(get_anova_table(aov), laov[["grupo:zona.escola"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.vocab.pre |
1 |
78 |
16.393 |
0.000 |
* |
0.174 |
| grupo |
1 |
78 |
0.859 |
0.357 |
|
0.011 |
| zona.escola |
1 |
78 |
0.000 |
0.994 |
|
0.000 |
| grupo:zona.escola |
1 |
78 |
1.176 |
0.282 |
|
0.015 |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(wdat, grupo), score.vocab.pos ~ zona.escola,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.escola), score.vocab.pos ~ grupo,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
|
Rural |
score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
78 |
1.415 |
0.161 |
0.161 |
ns |
|
Urbana |
score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
78 |
0.171 |
0.864 |
0.864 |
ns |
| Controle |
|
score.vocab.pre*zona.escola |
score.vocab.pos |
Rural |
Urbana |
78 |
0.748 |
0.456 |
0.456 |
ns |
| Experimental |
|
score.vocab.pre*zona.escola |
score.vocab.pos |
Rural |
Urbana |
78 |
-0.781 |
0.437 |
0.437 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.escola")),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.escola"]],
by=c("grupo","zona.escola","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.vocab |
pre |
pos |
158 |
-2.067 |
0.040 |
0.040 |
* |
| Controle |
Urbana |
time |
score.vocab |
pre |
pos |
158 |
-1.082 |
0.281 |
0.281 |
ns |
| Experimental |
Rural |
time |
score.vocab |
pre |
pos |
158 |
-0.229 |
0.819 |
0.819 |
ns |
| Experimental |
Urbana |
time |
score.vocab |
pre |
pos |
158 |
-1.190 |
0.236 |
0.236 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(wdat, "score.vocab.pos", c("grupo","zona.escola"), covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.vocab.pre","se.score.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- merge(ds, lemms[["grupo:zona.escola"]],
by=c("grupo","zona.escola"), suffixes = c("","'"))
}
| Controle |
Rural |
13 |
11.615 |
1.352 |
15.231 |
1.401 |
16.035 |
1.225 |
13.596 |
18.474 |
| Controle |
Urbana |
31 |
14.097 |
0.816 |
15.323 |
0.878 |
14.935 |
0.789 |
13.364 |
16.505 |
| Experimental |
Rural |
12 |
13.000 |
0.640 |
13.417 |
1.411 |
13.556 |
1.259 |
11.050 |
16.061 |
| Experimental |
Urbana |
27 |
13.296 |
0.747 |
14.741 |
0.850 |
14.737 |
0.839 |
13.067 |
16.407 |
Plots for ancova
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.escola", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.escola", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.vocab.pos", c("grupo","zona.escola"), aov, pwcs, covar = "score.vocab.pre",
theme = "classic", color = color[["grupo:zona.escola"]],
subtitle = which(aov$Effect == "grupo:zona.escola"))
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots[["grupo:zona.escola"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.vocab", c("grupo","zona.escola"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.escola"]])) >= 2)
plots[["grupo:zona.escola"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
facet.by = c("grupo","zona.escola"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "grupo", facet.by = "zona.escola", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "zona.escola", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.escola)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.escola"]])) >= 2)
res <- augment(lm(score.vocab.pos ~ score.vocab.pre + grupo*zona.escola, data = wdat))
if (length(unique(pdat[["zona.escola"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.976 0.121
if (length(unique(pdat[["zona.escola"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.escola)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 79 0.116 0.950
ANCOVA
and Pairwise for two factors
grupo:score.vocab.quintile
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["score.vocab.quintile"]]),],
"score.vocab.pos", c("grupo","score.vocab.quintile"))
pdat = pdat[pdat[["score.vocab.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["score.vocab.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["score.vocab.quintile"]] = factor(
pdat[["score.vocab.quintile"]],
level[["score.vocab.quintile"]][level[["score.vocab.quintile"]] %in% unique(pdat[["score.vocab.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","score.vocab.quintile")], pdat[,c("id","grupo","score.vocab.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab"]] <- c(pdat[["score.vocab.pre"]], pdat[["score.vocab.pos"]])
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
aov = anova_test(pdat, score.vocab.pos ~ score.vocab.pre + grupo*score.vocab.quintile)
laov[["grupo:score.vocab.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.vocab.quintile"]] <- emmeans_test(
group_by(pdat, grupo), score.vocab.pos ~ score.vocab.quintile,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, score.vocab.quintile), score.vocab.pos ~ grupo,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.vocab.quintile"]])
pwc <- pwc[,c("grupo","score.vocab.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.vocab.quintile")])]
}
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","score.vocab.quintile")),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.vocab.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "score.vocab.pos", c("grupo","score.vocab.quintile"), covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.vocab.quintile"), all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.vocab.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.vocab.quintile","n","mean.score.vocab.pre","se.score.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.vocab.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.vocab.quintile"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.vocab.pos ~ score.vocab.pre + grupo*score.vocab.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","score.vocab.quintile")], wdat[,c("id","grupo","score.vocab.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab"]] <- c(wdat[["score.vocab.pre"]], wdat[["score.vocab.pos"]])
ldat[["grupo:score.vocab.quintile"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
aov = anova_test(wdat, score.vocab.pos ~ score.vocab.pre + grupo*score.vocab.quintile)
laov[["grupo:score.vocab.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:score.vocab.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.vocab.pre |
1 |
72 |
5.551 |
0.021 |
* |
0.072 |
| grupo |
1 |
72 |
0.790 |
0.377 |
|
0.011 |
| score.vocab.quintile |
1 |
72 |
0.260 |
0.612 |
|
0.004 |
| grupo:score.vocab.quintile |
1 |
72 |
0.294 |
0.589 |
|
0.004 |
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.vocab.quintile"]] <- emmeans_test(
group_by(wdat, grupo), score.vocab.pos ~ score.vocab.quintile,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, score.vocab.quintile), score.vocab.pos ~ grupo,
covariate = score.vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.vocab.quintile"]])
pwc <- pwc[,c("grupo","score.vocab.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.vocab.quintile")])]
}
|
1st quintile |
score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
72 |
0.251 |
0.803 |
0.803 |
ns |
|
2nd quintile |
score.vocab.pre*grupo |
score.vocab.pos |
Controle |
Experimental |
72 |
1.007 |
0.317 |
0.317 |
ns |
| Controle |
|
score.vocab.pre*score.vocab.quintile |
score.vocab.pos |
1st quintile |
2nd quintile |
72 |
0.106 |
0.916 |
0.916 |
ns |
| Experimental |
|
score.vocab.pre*score.vocab.quintile |
score.vocab.pos |
1st quintile |
2nd quintile |
72 |
0.685 |
0.496 |
0.496 |
ns |
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","score.vocab.quintile")),
score.vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.vocab.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:score.vocab.quintile"]],
by=c("grupo","score.vocab.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
1st quintile |
time |
score.vocab |
pre |
pos |
146 |
-3.528 |
0.001 |
0.001 |
*** |
| Controle |
2nd quintile |
time |
score.vocab |
pre |
pos |
146 |
-0.986 |
0.326 |
0.326 |
ns |
| Experimental |
1st quintile |
time |
score.vocab |
pre |
pos |
146 |
-2.723 |
0.007 |
0.007 |
** |
| Experimental |
2nd quintile |
time |
score.vocab |
pre |
pos |
146 |
0.145 |
0.885 |
0.885 |
ns |
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "score.vocab.pos", c("grupo","score.vocab.quintile"), covar = "score.vocab.pre")
ds <- merge(ds[ds$variable != "score.vocab.pre",],
ds[ds$variable == "score.vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.vocab.quintile"), all.x = T, suffixes = c("", ".score.vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.vocab.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.vocab.quintile","n","mean.score.vocab.pre","se.score.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.vocab.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.vocab.quintile"]] <- merge(ds, lemms[["grupo:score.vocab.quintile"]],
by=c("grupo","score.vocab.quintile"), suffixes = c("","'"))
}
| Controle |
1st quintile |
21 |
9.429 |
0.481 |
13.190 |
0.804 |
15.158 |
1.265 |
12.638 |
17.679 |
| Controle |
2nd quintile |
19 |
15.789 |
0.511 |
16.895 |
1.144 |
14.928 |
1.301 |
12.335 |
17.522 |
| Experimental |
1st quintile |
19 |
10.368 |
0.335 |
13.421 |
1.183 |
14.808 |
1.159 |
12.498 |
17.118 |
| Experimental |
2nd quintile |
18 |
15.333 |
0.404 |
15.167 |
0.940 |
13.483 |
1.250 |
10.991 |
15.974 |
Plots for ancova
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "score.vocab.quintile", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:score.vocab.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["score.vocab.quintile"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "score.vocab.quintile", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:score.vocab.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.vocab.pos", c("grupo","score.vocab.quintile"), aov, pwcs, covar = "score.vocab.pre",
theme = "classic", color = color[["grupo:score.vocab.quintile"]],
subtitle = which(aov$Effect == "grupo:score.vocab.quintile"))
}
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
plots[["grupo:score.vocab.quintile"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.vocab", c("grupo","score.vocab.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2)
plots[["grupo:score.vocab.quintile"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
facet.by = c("grupo","score.vocab.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "grupo", facet.by = "score.vocab.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.vocab.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.vocab.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.pre", y = "score.vocab.pos", size = 0.5,
color = "score.vocab.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = score.vocab.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.vocab.quintile"))) +
ggplot2::scale_color_manual(values = color[["score.vocab.quintile"]]) +
ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2)
res <- augment(lm(score.vocab.pos ~ score.vocab.pre + grupo*score.vocab.quintile, data = wdat))
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.969 0.0547
if (length(unique(pdat[["score.vocab.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*score.vocab.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 73 1.04 0.379
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.vocab.pre |
44 |
13.364 |
13.0 |
5 |
23 |
4.725 |
0.712 |
1.437 |
6.25 |
YES |
0.329 |
-0.650 |
| Experimental |
|
|
|
|
score.vocab.pre |
39 |
13.205 |
13.0 |
6 |
22 |
3.427 |
0.549 |
1.111 |
4.00 |
YES |
0.445 |
-0.237 |
|
|
|
|
|
score.vocab.pre |
83 |
13.289 |
13.0 |
5 |
23 |
4.142 |
0.455 |
0.905 |
6.00 |
YES |
0.395 |
-0.271 |
| Controle |
|
|
|
|
score.vocab.pos |
44 |
15.295 |
15.0 |
4 |
26 |
4.878 |
0.735 |
1.483 |
6.25 |
YES |
0.295 |
-0.376 |
| Experimental |
|
|
|
|
score.vocab.pos |
39 |
14.333 |
14.0 |
6 |
25 |
4.544 |
0.728 |
1.473 |
4.50 |
NO |
0.554 |
0.122 |
|
|
|
|
|
score.vocab.pos |
83 |
14.843 |
14.0 |
4 |
26 |
4.720 |
0.518 |
1.031 |
5.00 |
YES |
0.426 |
-0.128 |
| Controle |
F |
|
|
|
score.vocab.pre |
23 |
14.348 |
14.0 |
7 |
23 |
4.519 |
0.942 |
1.954 |
7.00 |
YES |
0.342 |
-0.917 |
| Controle |
M |
|
|
|
score.vocab.pre |
21 |
12.286 |
12.0 |
5 |
23 |
4.818 |
1.051 |
2.193 |
5.00 |
YES |
0.418 |
-0.608 |
| Experimental |
F |
|
|
|
score.vocab.pre |
17 |
12.353 |
12.0 |
6 |
22 |
3.517 |
0.853 |
1.808 |
3.00 |
NO |
0.941 |
1.239 |
| Experimental |
M |
|
|
|
score.vocab.pre |
22 |
13.864 |
14.0 |
9 |
20 |
3.285 |
0.700 |
1.456 |
5.00 |
YES |
0.084 |
-1.211 |
| Controle |
F |
|
|
|
score.vocab.pos |
23 |
16.696 |
16.0 |
8 |
26 |
4.922 |
1.026 |
2.128 |
8.00 |
YES |
0.285 |
-1.082 |
| Controle |
M |
|
|
|
score.vocab.pos |
21 |
13.762 |
13.0 |
4 |
24 |
4.449 |
0.971 |
2.025 |
4.00 |
YES |
0.173 |
0.041 |
| Experimental |
F |
|
|
|
score.vocab.pos |
17 |
14.118 |
14.0 |
6 |
24 |
4.241 |
1.029 |
2.180 |
6.00 |
YES |
0.303 |
-0.153 |
| Experimental |
M |
|
|
|
score.vocab.pos |
22 |
14.500 |
14.0 |
7 |
25 |
4.857 |
1.036 |
2.154 |
3.75 |
NO |
0.625 |
-0.112 |
| Controle |
|
Rural |
|
|
score.vocab.pre |
11 |
10.727 |
10.0 |
5 |
16 |
3.467 |
1.045 |
2.329 |
4.00 |
YES |
-0.119 |
-1.265 |
| Controle |
|
Urbana |
|
|
score.vocab.pre |
22 |
15.545 |
15.5 |
7 |
23 |
4.667 |
0.995 |
2.069 |
6.75 |
YES |
-0.025 |
-1.075 |
| Experimental |
|
Rural |
|
|
score.vocab.pre |
15 |
13.733 |
13.0 |
6 |
22 |
4.026 |
1.040 |
2.230 |
5.50 |
YES |
0.212 |
-0.551 |
| Experimental |
|
Urbana |
|
|
score.vocab.pre |
14 |
12.429 |
11.5 |
9 |
18 |
2.954 |
0.789 |
1.706 |
4.75 |
YES |
0.491 |
-1.233 |
| Controle |
|
Rural |
|
|
score.vocab.pos |
11 |
15.364 |
13.0 |
10 |
26 |
4.864 |
1.466 |
3.267 |
5.00 |
NO |
0.956 |
-0.396 |
| Controle |
|
Urbana |
|
|
score.vocab.pos |
22 |
16.273 |
16.0 |
4 |
24 |
5.539 |
1.181 |
2.456 |
8.00 |
YES |
-0.252 |
-0.861 |
| Experimental |
|
Rural |
|
|
score.vocab.pos |
15 |
14.933 |
15.0 |
6 |
24 |
4.399 |
1.136 |
2.436 |
4.50 |
YES |
0.225 |
-0.118 |
| Experimental |
|
Urbana |
|
|
score.vocab.pos |
14 |
14.929 |
14.5 |
7 |
25 |
5.385 |
1.439 |
3.109 |
6.00 |
NO |
0.511 |
-0.669 |
| Controle |
|
|
Rural |
|
score.vocab.pre |
13 |
11.615 |
10.0 |
5 |
21 |
4.874 |
1.352 |
2.945 |
7.00 |
YES |
0.311 |
-1.180 |
| Controle |
|
|
Urbana |
|
score.vocab.pre |
31 |
14.097 |
13.0 |
6 |
23 |
4.541 |
0.816 |
1.666 |
6.50 |
YES |
0.431 |
-0.690 |
| Experimental |
|
|
Rural |
|
score.vocab.pre |
12 |
13.000 |
13.5 |
9 |
16 |
2.216 |
0.640 |
1.408 |
3.25 |
YES |
-0.184 |
-1.319 |
| Experimental |
|
|
Urbana |
|
score.vocab.pre |
27 |
13.296 |
12.0 |
6 |
22 |
3.881 |
0.747 |
1.535 |
6.00 |
YES |
0.411 |
-0.699 |
| Controle |
|
|
Rural |
|
score.vocab.pos |
13 |
15.231 |
15.0 |
8 |
26 |
5.052 |
1.401 |
3.053 |
5.00 |
NO |
0.666 |
-0.520 |
| Controle |
|
|
Urbana |
|
score.vocab.pos |
31 |
15.323 |
15.0 |
4 |
24 |
4.888 |
0.878 |
1.793 |
7.00 |
YES |
0.108 |
-0.480 |
| Experimental |
|
|
Rural |
|
score.vocab.pos |
12 |
13.417 |
14.0 |
6 |
24 |
4.889 |
1.411 |
3.106 |
7.00 |
YES |
0.462 |
-0.484 |
| Experimental |
|
|
Urbana |
|
score.vocab.pos |
27 |
14.741 |
14.0 |
7 |
25 |
4.417 |
0.850 |
1.747 |
4.50 |
NO |
0.641 |
0.189 |
| Controle |
|
|
|
1st quintile |
score.vocab.pre |
21 |
9.429 |
10.0 |
5 |
12 |
2.204 |
0.481 |
1.003 |
2.00 |
NO |
-0.630 |
-0.925 |
| Controle |
|
|
|
2nd quintile |
score.vocab.pre |
19 |
15.789 |
15.0 |
13 |
19 |
2.226 |
0.511 |
1.073 |
3.50 |
YES |
0.222 |
-1.461 |
| Experimental |
|
|
|
1st quintile |
score.vocab.pre |
19 |
10.368 |
11.0 |
6 |
12 |
1.461 |
0.335 |
0.704 |
1.00 |
NO |
-1.222 |
1.668 |
| Experimental |
|
|
|
2nd quintile |
score.vocab.pre |
18 |
15.333 |
15.0 |
13 |
19 |
1.715 |
0.404 |
0.853 |
2.75 |
YES |
0.499 |
-0.881 |
| Controle |
|
|
|
1st quintile |
score.vocab.pos |
21 |
13.190 |
13.0 |
4 |
22 |
3.683 |
0.804 |
1.676 |
3.00 |
YES |
-0.115 |
0.785 |
| Controle |
|
|
|
2nd quintile |
score.vocab.pos |
19 |
16.895 |
16.0 |
8 |
26 |
4.988 |
1.144 |
2.404 |
6.50 |
YES |
0.011 |
-1.015 |
| Experimental |
|
|
|
1st quintile |
score.vocab.pos |
19 |
13.421 |
13.0 |
6 |
25 |
5.157 |
1.183 |
2.485 |
5.50 |
NO |
0.733 |
-0.181 |
| Experimental |
|
|
|
2nd quintile |
score.vocab.pos |
18 |
15.167 |
15.0 |
9 |
25 |
3.989 |
0.940 |
1.984 |
2.00 |
NO |
0.646 |
0.283 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
grupo |
1 |
80 |
0.868 |
0.354 |
|
0.011 |
1 |
80 |
0.868 |
0.354 |
|
0.011 |
| 2 |
score.vocab.pre |
1 |
80 |
16.154 |
0.000 |
* |
0.168 |
1 |
80 |
16.154 |
0.000 |
* |
0.168 |
| 3 |
genero |
1 |
78 |
1.596 |
0.210 |
|
0.020 |
1 |
78 |
1.596 |
0.210 |
|
0.020 |
| 5 |
grupo:genero |
1 |
78 |
0.811 |
0.371 |
|
0.010 |
1 |
78 |
0.811 |
0.371 |
|
0.010 |
| 8 |
grupo:zona.participante |
1 |
57 |
0.967 |
0.329 |
|
0.017 |
1 |
57 |
0.967 |
0.329 |
|
0.017 |
| 10 |
zona.participante |
1 |
57 |
0.136 |
0.714 |
|
0.002 |
1 |
57 |
0.136 |
0.714 |
|
0.002 |
| 12 |
grupo:zona.escola |
1 |
78 |
1.176 |
0.282 |
|
0.015 |
1 |
78 |
1.176 |
0.282 |
|
0.015 |
| 14 |
zona.escola |
1 |
78 |
0.000 |
0.994 |
|
0.000 |
1 |
78 |
0.000 |
0.994 |
|
0.000 |
| 16 |
grupo:score.vocab.quintile |
1 |
72 |
0.294 |
0.589 |
|
0.004 |
1 |
72 |
0.294 |
0.589 |
|
0.004 |
| 18 |
score.vocab.quintile |
1 |
72 |
0.260 |
0.612 |
|
0.004 |
1 |
72 |
0.260 |
0.612 |
|
0.004 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
pre |
pos |
162 |
-2.034 |
0.044 |
0.044 |
* |
162 |
-2.034 |
0.044 |
0.044 |
* |
| Experimental |
|
|
|
|
pre |
pos |
162 |
-1.118 |
0.265 |
0.265 |
ns |
162 |
-1.118 |
0.265 |
0.265 |
ns |
|
|
|
|
|
Controle |
Experimental |
80 |
0.932 |
0.354 |
0.354 |
ns |
80 |
0.932 |
0.354 |
0.354 |
ns |
| Controle |
F |
|
|
|
pre |
pos |
158 |
-1.812 |
0.072 |
0.072 |
ns |
158 |
-1.812 |
0.072 |
0.072 |
ns |
| Controle |
M |
|
|
|
pre |
pos |
158 |
-1.089 |
0.278 |
0.278 |
ns |
158 |
-1.089 |
0.278 |
0.278 |
ns |
| Controle |
|
|
|
|
F |
M |
78 |
1.534 |
0.129 |
0.129 |
ns |
78 |
1.534 |
0.129 |
0.129 |
ns |
| Experimental |
F |
|
|
|
pre |
pos |
158 |
-1.171 |
0.243 |
0.243 |
ns |
158 |
-1.171 |
0.243 |
0.243 |
ns |
| Experimental |
M |
|
|
|
pre |
pos |
158 |
-0.480 |
0.632 |
0.632 |
ns |
158 |
-0.480 |
0.632 |
0.632 |
ns |
| Experimental |
|
|
|
|
F |
M |
78 |
0.195 |
0.846 |
0.846 |
ns |
78 |
0.195 |
0.846 |
0.846 |
ns |
|
F |
|
|
|
Controle |
Experimental |
78 |
1.219 |
0.226 |
0.226 |
ns |
78 |
1.219 |
0.226 |
0.226 |
ns |
|
M |
|
|
|
Controle |
Experimental |
78 |
-0.039 |
0.969 |
0.969 |
ns |
78 |
-0.039 |
0.969 |
0.969 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
57 |
0.979 |
0.332 |
0.332 |
ns |
57 |
0.979 |
0.332 |
0.332 |
ns |
| Controle |
|
Rural |
|
|
pre |
pos |
116 |
-2.368 |
0.020 |
0.020 |
* |
116 |
-2.368 |
0.020 |
0.020 |
* |
| Controle |
|
Urbana |
|
|
pre |
pos |
116 |
-0.525 |
0.600 |
0.600 |
ns |
116 |
-0.525 |
0.600 |
0.600 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
57 |
-0.424 |
0.673 |
0.673 |
ns |
57 |
-0.424 |
0.673 |
0.673 |
ns |
| Experimental |
|
Rural |
|
|
pre |
pos |
116 |
-0.716 |
0.476 |
0.476 |
ns |
116 |
-0.716 |
0.476 |
0.476 |
ns |
| Experimental |
|
Urbana |
|
|
pre |
pos |
116 |
-1.440 |
0.153 |
0.153 |
ns |
116 |
-1.440 |
0.153 |
0.153 |
ns |
|
|
Rural |
|
|
Controle |
Experimental |
57 |
1.125 |
0.265 |
0.265 |
ns |
57 |
1.125 |
0.265 |
0.265 |
ns |
|
|
Urbana |
|
|
Controle |
Experimental |
57 |
-0.258 |
0.797 |
0.797 |
ns |
57 |
-0.258 |
0.797 |
0.797 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
78 |
0.748 |
0.456 |
0.456 |
ns |
78 |
0.748 |
0.456 |
0.456 |
ns |
| Controle |
|
|
Rural |
|
pre |
pos |
158 |
-2.067 |
0.040 |
0.040 |
* |
158 |
-2.067 |
0.040 |
0.040 |
* |
| Controle |
|
|
Urbana |
|
pre |
pos |
158 |
-1.082 |
0.281 |
0.281 |
ns |
158 |
-1.082 |
0.281 |
0.281 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
78 |
-0.781 |
0.437 |
0.437 |
ns |
78 |
-0.781 |
0.437 |
0.437 |
ns |
| Experimental |
|
|
Rural |
|
pre |
pos |
158 |
-0.229 |
0.819 |
0.819 |
ns |
158 |
-0.229 |
0.819 |
0.819 |
ns |
| Experimental |
|
|
Urbana |
|
pre |
pos |
158 |
-1.190 |
0.236 |
0.236 |
ns |
158 |
-1.190 |
0.236 |
0.236 |
ns |
|
|
|
Rural |
|
Controle |
Experimental |
78 |
1.415 |
0.161 |
0.161 |
ns |
78 |
1.415 |
0.161 |
0.161 |
ns |
|
|
|
Urbana |
|
Controle |
Experimental |
78 |
0.171 |
0.864 |
0.864 |
ns |
78 |
0.171 |
0.864 |
0.864 |
ns |
| Controle |
|
|
|
1st quintile |
pre |
pos |
146 |
-3.528 |
0.001 |
0.001 |
*** |
146 |
-3.528 |
0.001 |
0.001 |
*** |
| Controle |
|
|
|
2nd quintile |
pre |
pos |
146 |
-0.986 |
0.326 |
0.326 |
ns |
146 |
-0.986 |
0.326 |
0.326 |
ns |
| Controle |
|
|
|
|
1st quintile |
2nd quintile |
72 |
0.106 |
0.916 |
0.916 |
ns |
72 |
0.106 |
0.916 |
0.916 |
ns |
| Experimental |
|
|
|
1st quintile |
pre |
pos |
146 |
-2.723 |
0.007 |
0.007 |
** |
146 |
-2.723 |
0.007 |
0.007 |
** |
| Experimental |
|
|
|
2nd quintile |
pre |
pos |
146 |
0.145 |
0.885 |
0.885 |
ns |
146 |
0.145 |
0.885 |
0.885 |
ns |
| Experimental |
|
|
|
|
1st quintile |
2nd quintile |
72 |
0.685 |
0.496 |
0.496 |
ns |
72 |
0.685 |
0.496 |
0.496 |
ns |
|
|
|
|
1st quintile |
Controle |
Experimental |
72 |
0.251 |
0.803 |
0.803 |
ns |
72 |
0.251 |
0.803 |
0.803 |
ns |
|
|
|
|
2nd quintile |
Controle |
Experimental |
72 |
1.007 |
0.317 |
0.317 |
ns |
72 |
1.007 |
0.317 |
0.317 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
44 |
13.364 |
0.712 |
15.295 |
0.735 |
15.261 |
0.654 |
13.960 |
16.562 |
44 |
13.364 |
0.712 |
15.295 |
0.735 |
15.261 |
0.654 |
13.960 |
16.562 |
0 |
| Experimental |
|
|
|
|
39 |
13.205 |
0.549 |
14.333 |
0.728 |
14.372 |
0.694 |
12.990 |
15.754 |
39 |
13.205 |
0.549 |
14.333 |
0.728 |
14.372 |
0.694 |
12.990 |
15.754 |
0 |
| Controle |
F |
|
|
|
23 |
14.348 |
0.942 |
16.696 |
1.026 |
16.235 |
0.910 |
14.423 |
18.048 |
23 |
14.348 |
0.942 |
16.696 |
1.026 |
16.235 |
0.910 |
14.423 |
18.048 |
0 |
| Controle |
M |
|
|
|
21 |
12.286 |
1.051 |
13.762 |
0.971 |
14.198 |
0.951 |
12.304 |
16.092 |
21 |
12.286 |
1.051 |
13.762 |
0.971 |
14.198 |
0.951 |
12.304 |
16.092 |
0 |
| Experimental |
F |
|
|
|
17 |
12.353 |
0.853 |
14.118 |
1.029 |
14.525 |
1.055 |
12.425 |
16.625 |
17 |
12.353 |
0.853 |
14.118 |
1.029 |
14.525 |
1.055 |
12.425 |
16.625 |
0 |
| Experimental |
M |
|
|
|
22 |
13.864 |
0.700 |
14.500 |
1.036 |
14.250 |
0.925 |
12.410 |
16.091 |
22 |
13.864 |
0.700 |
14.500 |
1.036 |
14.250 |
0.925 |
12.410 |
16.091 |
0 |
| Controle |
|
Rural |
|
|
11 |
10.727 |
1.045 |
15.364 |
1.466 |
16.968 |
1.467 |
14.030 |
19.905 |
11 |
10.727 |
1.045 |
15.364 |
1.466 |
16.968 |
1.467 |
14.030 |
19.905 |
0 |
| Controle |
|
Urbana |
|
|
22 |
15.545 |
0.995 |
16.273 |
1.181 |
15.137 |
1.037 |
13.060 |
17.215 |
22 |
15.545 |
0.995 |
16.273 |
1.181 |
15.137 |
1.037 |
13.060 |
17.215 |
0 |
| Experimental |
|
Rural |
|
|
15 |
13.733 |
1.040 |
14.933 |
1.136 |
14.828 |
1.201 |
12.424 |
17.233 |
15 |
13.733 |
1.040 |
14.933 |
1.136 |
14.828 |
1.201 |
12.424 |
17.233 |
0 |
| Experimental |
|
Urbana |
|
|
14 |
12.429 |
0.789 |
14.929 |
1.439 |
15.565 |
1.254 |
13.054 |
18.077 |
14 |
12.429 |
0.789 |
14.929 |
1.439 |
15.565 |
1.254 |
13.054 |
18.077 |
0 |
| Controle |
|
|
Rural |
|
13 |
11.615 |
1.352 |
15.231 |
1.401 |
16.035 |
1.225 |
13.596 |
18.474 |
13 |
11.615 |
1.352 |
15.231 |
1.401 |
16.035 |
1.225 |
13.596 |
18.474 |
0 |
| Controle |
|
|
Urbana |
|
31 |
14.097 |
0.816 |
15.323 |
0.878 |
14.935 |
0.789 |
13.364 |
16.505 |
31 |
14.097 |
0.816 |
15.323 |
0.878 |
14.935 |
0.789 |
13.364 |
16.505 |
0 |
| Experimental |
|
|
Rural |
|
12 |
13.000 |
0.640 |
13.417 |
1.411 |
13.556 |
1.259 |
11.050 |
16.061 |
12 |
13.000 |
0.640 |
13.417 |
1.411 |
13.556 |
1.259 |
11.050 |
16.061 |
0 |
| Experimental |
|
|
Urbana |
|
27 |
13.296 |
0.747 |
14.741 |
0.850 |
14.737 |
0.839 |
13.067 |
16.407 |
27 |
13.296 |
0.747 |
14.741 |
0.850 |
14.737 |
0.839 |
13.067 |
16.407 |
0 |
| Controle |
|
|
|
1st quintile |
21 |
9.429 |
0.481 |
13.190 |
0.804 |
15.158 |
1.265 |
12.638 |
17.679 |
21 |
9.429 |
0.481 |
13.190 |
0.804 |
15.158 |
1.265 |
12.638 |
17.679 |
0 |
| Controle |
|
|
|
2nd quintile |
19 |
15.789 |
0.511 |
16.895 |
1.144 |
14.928 |
1.301 |
12.335 |
17.522 |
19 |
15.789 |
0.511 |
16.895 |
1.144 |
14.928 |
1.301 |
12.335 |
17.522 |
0 |
| Experimental |
|
|
|
1st quintile |
19 |
10.368 |
0.335 |
13.421 |
1.183 |
14.808 |
1.159 |
12.498 |
17.118 |
19 |
10.368 |
0.335 |
13.421 |
1.183 |
14.808 |
1.159 |
12.498 |
17.118 |
0 |
| Experimental |
|
|
|
2nd quintile |
18 |
15.333 |
0.404 |
15.167 |
0.940 |
13.483 |
1.250 |
10.991 |
15.974 |
18 |
15.333 |
0.404 |
15.167 |
0.940 |
13.483 |
1.250 |
10.991 |
15.974 |
0 |